Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm

Jaber Soltani, Moosa Kalanaki and Mohammad Soltani

Modern Applied Science, 2016, vol. 10, issue 7, 29

Abstract: This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.

Date: 2016
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